Validated differentiation of Listeria monocytogenes by FT-IR spectroscopy using an artificial neural network based classifier

Listeria monocytogenes, a foodborne pathogen, poses a particular risk to vulnerable populations such as infants, the elderly, immunocompromised individuals, and pregnant women. Timely and accurate identification helps to prevent outbreaks of listeriosis and is essential for ensuring food safety and protection of public health.

In this webinar, Dr. Helene Oberreuter, food microbiologist and senior government councilor, will highlight a validated workflow for the differentiation of Listeria monocytogenes serogroups by FT-IR spectroscopy. The workflow consists of species identification by MALDI-TOF mass spectrometry (MALDI Biotyper^®) followed by serogroup differentiation with the IR Biotyper^® using a classifier which is based on an artificial neural network.

Key learning objectives

• Explore the use of FT-IR spectroscopy in food pathogen detection
• Discover a validated workflow for serogroup differentiation
• Learn how to apply an artificial neural network to pathogen classification
• Gain insights into the preselection of samples by FT-IR for whole genome sequencing

Who should attend?

Those working in food microbiology laboratories, reference laboratories and centers for food pathogens, state and public health laboratories, and centers for disease control and prevention

Certificate of attendance
All webinar participants can request a certificate of attendance, including a learning outcomes summary, for continuing education purposes.